Movement Enhancement toward Multi-Scale Video Feature Representation for Temporal Action Detection

Boundary localization is a challenging problem in Temporal Action Detection (TAD), in which there are two main issues. First, the submergence of movement feature, i.e. the movement information in a snippet is covered by the scene information. Second, the scale of action, that is, the proportion of action segments in the entire video, is considerably variable. In this work, we first design a Movement Enhance Module (MEM) to highlight movement feature for better action location, and then, we propose a Scale Feature Pyramid Network (SFPN) to detect multi-scale actions in videos. For Movement Enhance Module, firstly, Movement Feature Extractor (MFE) is designed to get the movement feature. Secondly, we propose a Multi-Relation Enhance Module (MREM) to grasp valuable information correlation both locally and temporally. For Scale Feature Pyramid Network, we design a U-Shape Module to model different scale actions, moreover, we design the training and inference strategy of different scales, ensuring that each pyramid layer is only responsible for actions at a specific scale. These two innovations are integrated as the Movement Enhance Network (MENet), and extensive experiments conducted on two challenging benchmarks demonstrate its effectiveness. MENet outperforms other representative TAD methods on ActivityNet-1.3 and THUMOS-14.